CIMMYT. Operational guidelines for assessing the impact of agricultural research livelihoods. Good practices from CIMMYT. Annex 3

Annex 3. The extra need to learn and change

The results of IAs are major drivers for organizational learning and change. IA processes are key components of organizational knowledge management. Evaluation methods based on traditional approaches inhibit rather than support innovation (Perrin, 2002). Although some projects may fail, the wider innovation process will be compensated by gains from the more successful projects and looking at portfolios of projects may be more appropriate than looking at individual ones, Averages can also mislead as they disguise what is truly happening. On the other hand, just selecting successful projects destroys credibility. Focusing on learning rather than on successes, and on approaches and mechanisms that enhance innovation itself, are advocated (Perrin, 2002).

There is also concern that much IA hasn’t made enough of a difference on research management or the impact orientation of research programs and institutes. Ekboir (2003), Horton and Mackay (2003), and Springer-Heinze (2002) agree that IA supports communication and decision making.

To serve this purpose, IA should be utilization-focused (Patton, 1995) and involve key intended users throughout the process. CGIAR centers, therefore, will get most out of IA if they engender a learning, risk-taking culture. Hence IA focal points should have a clear and formal mandate to support organizational learning and change, and not just the production of IA reports.

IA that is done with too much emphasis on compliance with pre-established rules and targets risks to trigger defensiveness, implementers playing it safe and other behavior (Perrin 2002). What is needed instead is people acting responsibly with regard to their mandate, asking the difficult questions, challenging their own assumptions, and doing their best possible thinking.

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